There are several adaptation techniques for NMT. [1]} structure them into two main groups, data centric (techniques which involve augmenting or modifying the training data) and model centric (techniques which involve modifying the architecture or the procedure with which the model is trained). In this paper, we use a combination of both as we use data selection methods (data centric) and fine-tuning (model centric).
